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- Kernel_methods abstract "In computer science, kernel methods are a class of algorithms for pattern analysis, whose best known member is the support vector machine (SVM). The general task of pattern analysis is to find and study general types of relations (for example clusters, rankings, principal components, correlations, classifications) in datasets. For many of these tasks, data have to be represented as feature vectors, but kernel methods replace this representation by similarities to other data points.Kernel methods owe their name to the use of kernel functions, which enable them to operate in a high-dimensional, implicit feature space without ever computing the coordinates of the data in that space, but rather by simply computing the inner products between the images of all pairs of data in the feature space. This operation is often computationally cheaper than the explicit computation of the coordinates. This approach is called the kernel trick. Kernel functions have been introduced for sequence data, graphs, text, images, as well as vectors.Algorithms capable of operating with kernels include the kernel perceptron, support vector machines (SVM), Gaussian processes, principal components analysis (PCA), canonical correlation analysis, ridge regression, spectral clustering, linear adaptive filters and many others. Any linear model can be turned into a non-linear model by applying the "kernel trick" to the model: replacing its features (predictors) by a kernel function.Most kernel algorithms are based on convex optimization or eigenproblems, are computationally efficient and statistically well-founded. Typically, their statistical properties are analyzed using statistical learning theory (for example, using Rademacher complexity).".
- Kernel_methods wikiPageExternalLink ?n=Main.KernelMethods.
- Kernel_methods wikiPageExternalLink www.kernel-machines.org.
- Kernel_methods wikiPageExternalLink www.support-vector-machines.org.
- Kernel_methods wikiPageID "3424576".
- Kernel_methods wikiPageRevisionID "603906357".
- Kernel_methods hasPhotoCollection Kernel_methods.
- Kernel_methods subject Category:Classification_algorithms.
- Kernel_methods subject Category:Geostatistics.
- Kernel_methods subject Category:Kernel_methods_for_machine_learning.
- Kernel_methods type Ability105616246.
- Kernel_methods type Abstraction100002137.
- Kernel_methods type Act100030358.
- Kernel_methods type Activity100407535.
- Kernel_methods type Algorithm105847438.
- Kernel_methods type ClassificationAlgorithms.
- Kernel_methods type Cognition100023271.
- Kernel_methods type Event100029378.
- Kernel_methods type KernelMethodsForMachineLearning.
- Kernel_methods type Know-how105616786.
- Kernel_methods type Method105660268.
- Kernel_methods type Procedure101023820.
- Kernel_methods type PsychologicalFeature100023100.
- Kernel_methods type Rule105846932.
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- Kernel_methods comment "In computer science, kernel methods are a class of algorithms for pattern analysis, whose best known member is the support vector machine (SVM). The general task of pattern analysis is to find and study general types of relations (for example clusters, rankings, principal components, correlations, classifications) in datasets.".
- Kernel_methods label "Kernel methods".
- Kernel_methods sameAs m.09bs7j.
- Kernel_methods sameAs Kernel_methods.
- Kernel_methods wasDerivedFrom Kernel_methods?oldid=603906357.
- Kernel_methods isPrimaryTopicOf Kernel_methods.